ML Platform Engineer

As an ML Platform Engineer at Pipedrive, your main responsibilities will include building and managing our cloud-based infrastructure for ML components on AWS, designing an end-to-end ML workflow infrastructure, and ensuring the health and performance of deployed machine learning models. You'll also be involved in integrating third-party services, documenting processes for knowledge sharing, and troubleshooting any issues that arise.

To succeed as an ML Platform Engineer at Pipedrive, candidates should ideally have a Bachelor or Master's degree in computer science or a related field, along with 3+ years of software development experience. Familiarity with cloud computing and experience with tools like Apache Spark and AWS are crucial. Knowledge of programming languages such as Python, Go, or Scala will also lend you an advantage.

In the ML Platform Engineer role at Pipedrive, you'll work extensively with AWS for cloud infrastructure management, utilize distributed systems like Apache Spark and Flink for large-scale data processing, and engage with configuration management tools such as Terraform and Ansible. You might also extend your knowledge of MLOps tooling while contributing to our ML systems.

Yes, Pipedrive offers flexibility in its work environment! While the role is based in Tartu or Tallinn, we support hybrid work models allowing you to balance remote work with in-office collaboration, ensuring you stay connected with your team and our projects.

The company culture at Pipedrive is dynamic and inclusive, supporting personal growth and collaboration. As an ML Platform Engineer, you'll be part of a diverse team that values getting work done while having fun. With opportunities for professional development, inclusive policies, and a lively work environment, you'll feel at home while making a difference.

When answering this question, highlight any projects where you utilized AWS services, focusing on specific tools like S3, Lambda, or EC2 that relate to ML platform infrastructure. Emphasize your understanding of security, compliance, and cost optimization in cloud applications.

Discuss your approach to troubleshooting by mentioning specific tools or techniques such as performance monitoring and logging frameworks. Include examples where you effectively identified and resolved issues in a timely manner to restore model functionality.

Describe your experience using tools like Terraform or Ansible for infrastructure management. Illustrate how you've set up automated deployments, version control, and the benefits of maintaining infrastructure as code for consistent and repeatable environments.

You should cover aspects such as data management, feature extraction, model training and validation, deployment strategies, and performance monitoring. Use specific examples from your experience to highlight how you’ve implemented efficient ML workflows.

Detail your involvement with systems like Apache Spark or Flink, focusing on how you utilized these tools to process large datasets. Talk about the architectures and strategies you employed to ensure efficient data flows and real-time processing capabilities.

If applicable, share your knowledge of MLOps tools and practices you’ve used in prior roles. Discuss how they improve the development and deployment lifecycle of machine learning models and foster collaboration between data scientists and operations teams.

Talk about security best practices, such as data encryption, access controls, and compliance with regulations like GDPR. Provide examples of incidents where you had to ensure compliance and the measures you took to maintain security.

Elaborate on your skills in Python, Go, or Scala, providing concrete examples of how you’ve used these languages to develop ML applications or tools. Emphasize your familiarity with libraries relevant to machine learning.

When asked about agile practices, share details about your role in a Scrum or Kanban team, perhaps focusing on how your contributions as an ML Platform Engineer helped prioritize tasks, manage workloads, or meet sprint deadlines.

Reflect on what drives you professionally and personally. This could include a passion for problem-solving, a desire to innovate in ML technology, or a commitment to enhancing team collaboration and efficiency within a dynamic work environment.